Sub field moisture model improvement using overland flow modeling with shallow water computations

ABSTRACT

Subfield moisture model improvement in generating overland flow modeling using shallow water calculations and kinematic wave calculations is disclosed. In an embodiment, a computer-implemented data processing method comprises: receiving precipitation data and infiltration data for an agricultural field; obtaining surface water depth data, surface water velocity data, and surface water discharge data for the same agricultural field; determining subfield geometry data for the agricultural field; executing a plurality of water calculations and wave calculations using the subfield geometry data to generate an overland flow model that includes moisture levels for the agricultural field; based on, at least in part, the overland flow model, generating and causing displaying a visual graphical image of the agricultural field comprising a plurality of color pixels having color values corresponding to the moisture levels determined for the agricultural field. Output of the overland flow model is provided to control computers of seeders, planters, fertilizer spreaders, harvesters, or combines to control seeding, planting, fertilizing or irrigation activities in the field.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as acontinuation of application Ser. No. 16/555,267, filed Aug. 29, 2019,which claims the benefit under 35 U.S.C. § 119(e) of provisionalapplication 62/725,884, filed Aug. 31, 2018, the entire contents ofwhich is hereby incorporated by reference for all purposes as if fullyset forth herein. The applicants hereby rescind any disclaimer of claimscope in the parent applications or the prosecution history thereof andadvise the USPTO that the claims in this application may be broader thanany claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2021 The Climate Corporation.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is computer-assistedvisualization of geophysical data in the field of agriculture, includingvisualization of overland water flows based on subfield moisture modelsgenerated for agricultural fields at the subfield scale. Anothertechnical field is computer-implemented techniques for executing shallowwater calculations and kinematic wave calculations over digitalelevation model data to generate subfield moisture models.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Variations in soil moisture in subfield regions of agricultural fieldsmay cause variations in nutrient levels in the fields and thusvariations in patterns in crop yields. While many computer-based toolshave been already developed for monitoring the nutrient levels in thesoil, the tools usually rely on low-granularity topographical maps ofthe fields, and therefore the nutrient level maps generated by the toolscan only provide low-granularity information.

Examples of low-granularity topographical maps are the maps that depictfields at a zone-based resolution. A zone is usually a non-uniformagricultural area that may be inconsistent in nature and may includeseveral subfields having, for example, distinct water retentioncharacteristics and different irrigation layouts. A zone usuallyincludes several subfields. Thus, a subfield-based map is usually ahigher-granularity map than a zone-based map.

Nutrient level maps determined at a zone level may be inadequate becausethe crop growers might prefer receiving the maps generated at a subfieldlevel. maps generated at a zone level, but not at a subfield level,cannot adequately capture hydrologic differences between the subfieldswithin a zone. This is especially the case because individual zones tendto lie within a narrow elevation range: there is little overland flowwithin a zone, but a lot between neighboring zones. The zone-based mapsare therefore unlikely to capture, for example, subfield-specificinformation about the amounts of nitrogen washed out by the waterflowing through the individual subfields or the nitrogen levels in theindividual subfields.

Thus, there is a need for a tool that is configured to generate asubfield-based soil moisture model for a high-granularity topographicalmap of an agricultural field, and that can be used to determinehydrologic fluxes, soil moisture levels and nutrient levels for ahigh-granularity map of the field.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts an example workflow for determining an overland flowmodel for an agricultural field.

FIG. 8A depicts an example flowchart for determining an overland flowmodel for an agricultural field using finite volume approaches.

FIG. 8B depicts an example approach for solving shallow water equationsusing a multi-core graphics processing unit.

FIG. 8C depicts an example flowchart for solving shallow water equationsfor an agricultural field using a multi-core graphics processingapproach.

FIG. 9 depicts an example flowchart for determining an overland flowmodel for an agricultural field.

FIG. 10 is a block diagram that illustrates an example computer systemconfigured to generate and display graphical user interface configuredto interact with a subfield moisture modeling tool.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM        -   2.1. STRUCTURAL OVERVIEW        -   2.2. APPLICATION PROGRAM OVERVIEW        -   2.3. DATA INGEST TO THE COMPUTER SYSTEM        -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING        -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   3. IMPROVED SUBFIELD MOISTURE MODELING TECHNIQUES        -   3.1. OVERLAND FLOW MODELS FOR AGRICULTURAL FIELDS        -   3.2. PRECIPITATION AND INFILTRATION DATA        -   3.3. TOPOGRAPHY PROCESSING    -   4. EXAMPLE APPLICATIONS    -   5. BENEFITS OF CERTAIN EMBODIMENTS    -   6. EXTENSIONS AND ALTERNATIVES

1. General Overview

In an embodiment, an approach and a tool are described for generating asubfield-based soil moisture model from a high-granularity topographicalmap of an agricultural field. The subfield-based soil moisture model maybe used in agricultural applications to, for example, predict hydrologicfluxes in the subfields of the field, and to determine soil moisture andnutrient levels at the field-pixel level of the field.

Subfield variations in soil moisture in agricultural fields are known tocause spatially varying leaching of nutrients in the fields andvariations in patterns in crop yields. Therefore, to accuratelydetermine or predict the crop yields, the variations in soil moistureneed to be captured per subfields and based on high-granularity maps ofthe fields.

Water contributions from subfield overland flows and shallow water tableflows in a field may significantly alter, for example, nitrogen levelsin the soil. While the overland flows often provide same-dayredistribution of water across the field, the shallow water tables oftenprovide multiple-day redistribution of the water. Because the overlandflows and the shallow water tables impact the soil moisture distributionand nitrogen level distribution differently over time, thetime-dependent differences between the overland flows and the shallowwater tables need to be modeled accurately.

In an embodiment, an approach allows modeling subfield flows in anagricultural field. The approach provides improvements over theconventional approaches because it involves fewer computation andstorage resources than the conventional approaches. The approach allowsgenerating a subfield moisture model and using the generated model todetermine moisture levels for a high-granularity topographical map ofthe field.

In an embodiment, a method and a system allow modeling overland flows byusing the highest-resolution elevation data available for a field. Themodels of the overland flows may be used to generate graphicalrepresentations of the flows. The graphical representations of the flowsmay be used by crop growers to optimize the agricultural practices forthe field to achieve the highest possible yield. The models may be alsoused to predict the dynamic spatial extent of the runoff waterproduction and water transfer for the field. The predicted informationmay be used to help the crop growers to, for example, adjust soilfertilizer dosages and fertilization schedules.

In an embodiment, an approach allows determining dynamic spatialcharacteristics of runoff water production and water transfer for anagricultural field. The characteristics are determined based onmathematical models derived by solving mathematical equations overtopographical data, soil data, soil moisture data, and precipitationdata.

Mathematical equations used to determine spatial characteristics of therunoff water production and water transfer in an agricultural field mayinclude shallow water equations and kinematic wave equations. Theequations are generally nonlinear, and therefore, finite difference andfinite volume numerical methods may be used to solve the equations. Thederived solutions are used to determine the spatial characteristics forthe runoff water production and water transfer in the field.

Subfield geometry for an agricultural field may be generated based on aninput topographical (elevation) dataset provided for the field. Thesubfield geometry and the spatial characteristics may be used todetermine surface water depths, water flow velocities, and waterdischarges. The surface water depths, velocities and discharges may becomputed for each pixel of the subfield geometry, and for each event,such as a rainstorm or other types of precipitation. The computed valuesmay be used to generate infiltration profiles for the field and theevents.

Infiltration profiles and events generated for an agricultural field maybe used to improve predictions of hydrologic flux. The predictions maybe used to generate notifications indicating the presence of standingsurface water or surface water head at subfields of the field. Receivingthe notifications may be useful to crop growers to identify thesubfields susceptible to, for example, ponding water. Furthermore, thegrowers may use the notifications to determine the subfields in thefield that are prone to accumulating water after strong rainfalls orstorms.

Model predictions of hydrologic fluxes may be used to improve monitoringnitrogen levels in a field and to help determining accurate applicationsof nitrogen to the field to help to optimize crop yields expected fromthe field in the future. In an embodiment, the predictions may beprovided to control computers of agricultural machines, such as afertilizer spreader, dispatched in the field to adjust the nitrogenapplication instructions executed by the machines as the machines applyfertilizers to the field.

Model output predictions may be captured in a model output layer for anagricultural field, and the model output may be used by agriculturalscripting tools as an independent variable, i.e., a covariant, togenerate and modify agricultural prescriptions for agriculturalpractices for managing the field. For example, the model outputpredictions may be provided to a control computer of an irrigationsystem installed in the field; based on the provided predictions, theirrigation system may adjust its watering settings and the amounts ofwater sprayed over the field.

Model output predictions may be also provided to control computers ofagricultural machines, such as planters, seeders, harvesters, combinesand cultivators, to adjust the paths within the field that the machineswill follow to plant seeds or harvest crops to avoid areas that,according to the provided predictions, are covered with standing wateror mud. The predictions may be also used by the planters and seeders toadjust, based on the provided predictions, amounts of seeds to beplanted by the seeders in the field, so that the areas with standingwater receive a different application of seeds than the areas withoutstanding water.

Model output predictions may be used by an intelligence computer systemto generate agricultural prescriptions, status reports, summaries, andother documents. The predictions may be used to, for example, forecastand explain yield variability, and how the yield variability is impactedby the predicted changes in soil moisture conditions in the field.Furthermore, the predictions may be used to generate explanations forthe yield variability between wet years and dry years. To generate theexplanations, the intelligence computer system may use the predictionsthat include information about the subfield soil moisture levels for thefield and information about boundaries of the watersheds formed in thefield to explain why the crop yield from some subfields is low.

2. Example Agricultural Intelligence Computer System

2.1. Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorus,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1 . The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,model and field data repository 160, and code instructions 180. “Layer,”in this context, refers to any combination of electronic digitalinterface circuits, microcontrollers, firmware such as drivers, and/orcomputer programs or other software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

In an embodiment, code instructions 180 comprise slope and curvaturemodeling instructions 136, shallow water instructions 137, kinematicwave instructions 138, and overland flow modeling instructions 139.Additional code instructions may also be included. Slope and curvaturemodeling instructions 136 may be used to generate, based onhigh-resolution geometry data for a field, a digital model capturing theslopes and curvatures within the field. Shallow water instructions 137may be used to calculate the shallow water equations using the slope andcurvature model generated based on the high-resolution geometry for thefield. Kinematic wave instructions 138 may be used to calculate thekinematic wave equations using the slope and curvature model generatedbased on the high-resolution geometry for the field. Overland flowmodeling instructions 139 may be used to generate an overland flow modelfor the field.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object-oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5 , a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil management, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse in other operations. After aprogram has been created, it may be conceptually applied to one or morefields and references to the program may be stored in digital storage inassociation with data identifying the fields. Thus, instead of manuallyentering identical data relating to the same nitrogen applications formultiple different fields, a user computer may create a program thatindicates a particular application of nitrogen and then apply theprogram to multiple different fields. For example, in the timeline viewof FIG. 5 , the top two timelines have the “Spring applied” programselected, which includes an application of 150 lbs. N/ac in early April.The data manager may provide an interface for editing a program. In anembodiment, when a particular program is edited, each field that hasselected the particular program is edited. For example, in FIG. 5 , ifthe “Spring applied” program is edited to reduce the application ofnitrogen to 130 lbs. N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5 , the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6 , a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6 . To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4 . The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more smartphones, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multi-lateration of radiosignals, the global positioning system (GPS), Wi-Fi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2 , each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shapefiles,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, email with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2 , in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or Wi-Fi-based position or mapping apps that areprogrammed to determine location based upon nearby Wi-Fi hotspots, amongothers.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview—Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset-models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop, where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general-purpose microprocessor.

Computer system 400 also includes a main memory 406, such as arandom-access memory (RAM) or other dynamic storage device, coupled tobus 402 for storing information and instructions to be executed byprocessor 404. Main memory 406 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 404. Such instructions, whenstored in non-transitory storage media accessible to processor 404,render computer system 400 into a special-purpose machine that iscustomized to perform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated-services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through theworld-wide packet data communication network now commonly referred to asthe “Internet” 428. Local network 422 and Internet 428 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 420 and through communication interface 418, which carrythe digital data to and from computer system 400, are example forms oftransmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Improved Subfield Moisture Modeling Techniques

Nitrogen levels in soil of an agricultural field may be impacted bywater accumulated in the field due to subfield overland flows andpresence of shallow water tables. The overland flows often providesame-day redistributions of fluxes across the field, whileredistribution of the shallow water tables may often take several days.Because of the temporal differences in the water redistributions, boththe overland flow redistribution and the sub-surface flow redistributionneed to be modelled accurately to obtain the models that can be usefulto the crop growers.

FIG. 7 depicts an example workflow for determining an overland flowmodel for an agricultural field. FIG. 8A depicts an example flowchartfor determining an overland flow model for an agricultural field. FIG.8B depicts an example approach for solving shallow water equations usinga graphics processing unit. FIG. 8C depicts an example flowchart fordetermining an overland flow model for an agricultural field using amulti-core graphics processing approach. FIG. 9 depicts an exampleflowchart for determining an overland flow model for an agriculturalfield. FIG. 10 is a block diagram that illustrates an example computersystem configured to generate and display graphical user interfaceconfigured to interact with a subfield moisture modeling tool. In anembodiment, the functions that are described herein in relation to FIG.7 , FIG. 8A, FIG. 8B, FIG. 8C, FIG. 9 , and FIG. 10 may be implementedusing code instructions 180, described in FIG. 1 .

FIG. 7 depicts an example workflow for determining an overland flowmodel for an agricultural field. The example workflow may start withobtaining high-resolution geometry data 702 for the field.High-resolution geometry data 702 may be used to generate ahigh-resolution digital elevation model (“DEM”) 704 for the field.High-resolution DEM 704 may have a fine-grid pixel resolution. Forexample, it may be based on a 5 m by 5 m pixel grid.

High-resolution DEM 704 may be used to generate a high-resolution,filtered digital elevation model (“FDEM”) 706. The filtering may includeapplying nonlinear anisotropic filtering techniques to remove so callednoise data or outliers, and to smooth the data points of high-resolutionDEM 704. The nonlinear anisotropic filtering techniques may includefinite difference and finite volume numerical methods to performfiltering of the data points in the high-resolution DEM 704.

Based on high-resolution, FDEM 706, slopes and curvature data 708 forthe field is determined. A slope, in this context, is an elevatedgeological formation. For example, a slope may be a stretch of groundforming a natural or artificial incline in the field. A curvature, inthis context, is a characteristic or a measure of a slope.

Based on extracted slopes and curvature data 708 and based on soil data,precipitation data and infiltration data 718 for the field, and a waterflow accumulation model 716 is generated. In an embodiment, water flowaccumulation model 716 corresponds to an overland flow model. Model 716may be generated using a D-infinity approach, and on a pixel-basis.

Based on extracted slopes and curvatures data 708, flow direction data710 for the data is determined.

Based on flow directions data 710, flow accumulation areas data 712 isdetermined. That data captures the information about standing waters.

Based on flow accumulation areas data 712, watershed boundaries andwater outlets 714 caused by the precipitation received in the field aredetermined.

Based on watershed boundaries and water outlets 714, overland flow model716 may be refined by adding the watershed boundaries component and thewater outlets component to overland flow model 716. At this point,overland flow model 716 captures information about the same-dayredistributions of fluxes of water across the field and capturesinformation about the watershed identification data.

The information about the watershed identification data and the overlandflow model 716 may be used to execute shallow water calculations 720 forthe watersheds and the model. Executing shallow water calculations 720may include solving one or more shallow water flow equations withrespect to the pixel-based overland flow model. This may also includemodeling covariates, such as a surface water depth parameter, a surfacewater velocity parameter, and a surface water discharge layer parameteras a function of space and time for the observed weather conditions.Once execution of shallow water calculations 720 is completed, theresults may be used to update overland flow model 716, which in turn maybe used to generate a subfield soil moisture model.

The information about the watershed identification data and overlandflow model 716 may be used to execute kinematic wave calculations 728.Executing kinematic wave calculations 728 may include solving one ormore kinematic wave equations with respect to the pixel-based overlandflow model. It may also include modeling covariates, such as a surfacewater depth parameter, a surface water velocity parameter, and a surfacewater discharge layer parameter as a function of space and time for theobserved weather conditions. Once execution of kinematic wavecalculations 728 is completed, the results may be used to updateoverland flow model 716, which in turn may be used to regenerate thesubfield soil moisture model.

The subfield soil moisture model may be used to generate answers toqueries for moisture levels for certain points of the field. The queryand answer functionalities may be provided via a graphical userinterface. Using the query functionalities, a user may enter, forexample, a query for moisture level information for a certain point ofinterest in the field. In response to the query, the subfield soilmoisture model is accessed, the soil moisture level information for thecertain point from the model is extracted, and the extracted soilmoisture level information is displayed as a response to the query.

The subfield soil moisture model may be also used to provide answers toqueries for nitrogen levels for certain points of the field. The queryfunctionalities may be provided via a graphical user interface, such asthe interface described above. Using the query functionalities, a usermay enter, for example, a query for nitrogen level information for acertain point of interest in the field. In response to the query, thesystem may access the subfield soil moisture model, extract the soilmoisture level information for the certain point from the model, use theextracted soil moisture level information to determine a nitrogen levelfor the certain point, and display the determined nitrogen levelinformation as a response to the query.

3.1. Overland Flow Models for Agricultural Fields

FIG. 8A depicts an example flowchart for determining an overland flowmodel for an agricultural field. The process depicted in FIG. 8Acaptures the processing described in blocks 714-726 of FIG. 7 . Theprocess may be executed by an agricultural intelligence computer system130 described in FIG. 1 . To perform the process depicted in FIG. 8A, anagricultural intelligence computer system 130 may execute codeinstructions 180, such as slopes and curvature modeling instructions136, shallow water instructions 137, kinematic wave instructions 138,and overland flow modeling instructions 139. For the clarity of thedescription, agricultural intelligence computer system 130 is referredto as an AIC system, or a system.

In step 802, an AIC system receives precipitation and water infiltrationdata for an agricultural field. This step corresponds to determiningprecipitation and infiltration data 718, described in FIG. 7 .

The received data may indicate the amounts of precipitation recorded forthe field and may be represented as a mapping between a pixel-basedgeometry map of the field and the corresponding precipitation data. Thereceived data may also include the water infiltration data thatindicates a measure of the infiltration rate at which the soil is ableto absorb rainfall or irrigation water. The infiltration data may berepresented, for example, as a mapping between pixels of the pixel-basedgeometry map of the field and the corresponding infiltration rates.

In step 804, the AIC system executes shallow water flow equations andkinematic wave equations over subfield geometry data. As depicted inFIG. 8A, this step may include several sub-steps, such as sub-steps806-814.

In step 806, the AIC system receives extracted slopes and curvature datafor subfield geometry data for the field. This sub-step corresponds toreceiving extracted slopes and curvatures data 708, described in FIG. 7.

In step 808, the AIC system determines surface water depths for thefield. This step corresponds to determining surface water depth 726,described in FIG. 7 . A surface water depth for a particular areacovered with water may be measured in inches, centimeters, and the like.It indicates the depth of the standing water detected in the particulararea.

In step 810, the AIC system determines surface water velocity data andsurface water discharge data. This step corresponds to determiningsurface water velocity data 724 and surface water discharge data 722,described in FIG. 7 . The data may be provided to the AIC system in aform of data tables containing empirically determined information.

In step 812, the AIC system executes one or more shallow water flowequations over the subfield geometry data. This step corresponds toexecuting shallow water calculations 720, described in FIG. 7 .

In an embodiment, the shallow water equations are derived from adepth-averaged integration of the Navier-Stokes equations. Morespecifically, the shallow water flow equations are simplifications ofthe Navier-Stokes equations to make the equations appropriate formodeling an open-channel water flow for an entire watershed. That meansthat the horizontal length scale in the equations is much greater thanthe vertical scale. It is assumed herein that the fluid isincompressible. The shallow water equations may be expressed in terms ofconservation of the mass and momentum.

In one dimension, the conservation of mass equation can be written as:

$\begin{matrix}{{\frac{\partial h}{\partial t} + \frac{\partial{uh}}{\partial x}} = {P - I}} & (1)\end{matrix}$which is equivalent to:

$\begin{matrix}{{\frac{\partial h}{\partial t} + \frac{\partial q}{\partial x}} = {P - I}} & (2)\end{matrix}$where h is the flow depth (L), t is the time (T), u is thedepth-averaged velocity in the x direction (L/T), q is the flux densityin the x direction (L²/T), x is the longitudinal distance (L), P is theincoming precipitation (L/T), and I is the infiltration (L/T).

The one-dimensional conservation of momentum equation can be written as:

$\begin{matrix}{{\frac{\partial u}{\partial t} + {u\frac{\partial u}{\partial x}} + {g\frac{\partial h}{\partial x}} + {g( {S_{0x} + S_{fx}} )}} = 0} & (3)\end{matrix}$where g is the acceleration due to gravity (L²/T), S_(fx) is the energyslope in the x direction, and Sox is the ground slope in the xdirection.

The first three terms in equation (3), comprising local acceleration,convective acceleration, and pressure force, are negligible incomparison to the last two terms representing gravitational and frictionforces. Therefore, equation (3) may be simplified by, for example,setting negligible forces to zero. This simplification leads to thekinematic wave approximation.

In step 814, the AIC system executes one or more kinematic waveapproximation equations over the subfield geometry data. This stepcorresponds to executing kinematic wave calculations 728, described inFIG. 7 . To determine kinematic wave approximations, it is assumed thatthe energy slope and the ground slope are equal. The assumption that theenergy slope and the ground slope are equal is reasonable especially fora steep topography. Therefore, such approximations can be successfullyapplied to, for example, mountain-based watersheds.

Solving a kinematic wave equation amounts to solving a modified equation(1), in which a flow can be expressed as a function of depth by usingthe Manning equations:

$\begin{matrix}{{Q = {A \cdot V}},} & (4) \\{Q = {A \cdot \frac{h^{2/3} \cdot \sqrt{S}}{n_{eff}}}} & (5)\end{matrix}$The relations expressed in equations (4)-(5) may be used to express qvalues as a function of u and h.

In an embodiment, it is assumed that the kinematic wave equation issolved over a 2D grid of geometry data for the field. The kinematic waveequation based on equation (1) in a 2D domain may be expressed as:

$\begin{matrix}{{\frac{\partial h}{\partial t} + \frac{\partial q_{x}}{\partial x} + \frac{\partial q_{y}}{\partial y}} = {P - I}} & (6)\end{matrix}$

Kinematic wave equations expressed in a 2D domain, such as equation (6),may be solved using many different approaches. One of the approachesuses a first order explicit scheme. Another approach is based on afinite volume method. Other approach utilizes hardware capabilities of amultiple-core graphics processing unit (“GPU”). These approaches aredescribed below. However, equation (6) may be also solved using otherapproaches.

3.1.1. First Order Explicit Methods for Solving Wave Equations

Equation (6) may be solved using a first order explicit scheme for timeand a second order central differencing scheme in space. Therefore,equation (6) may be rewritten as:

$\begin{matrix}{{\frac{h_{({i,j})}^{t + 1} - h_{({i,j})}^{t}}{\Delta\; t} + \frac{q_{x{({{i + 1},j})}}^{t} - q_{x{({{i - 1},j})}}^{t}}{2\Delta\; x} + \frac{q_{y{({{i + 1},j})}}^{t} - q_{y{({{i - 1},j})}}^{t}}{2\Delta\; y}} = {P - I}} & (7)\end{matrix}$where Δt is the time step of calculation; Δx and Δy are the lengths ofthe calculation grid unit in the x direction and the y direction,respectively.

The water flux (q) in the x and y directions may be expressed using theManning equation:

$\begin{matrix}{{q_{x} = \frac{h^{2/3}S_{0x}^{1/2}}{n_{eff}}},} & (8) \\{q_{y} = \frac{h^{2/3}S_{0y}^{1/2}}{n_{eff}}} & (9)\end{matrix}$

3.1.2. Finite Volume Methods for Solving Wave Equations

In an embodiment, the AIC system is programmed with instructions tosolve kinematic wave equations expressed in a 2D domain, such asequation (6), using a finite volume approach. Using programming for thefinite volume approach, the AIC system is programmed to solve the 2Dkinematic wave equation (6) by selecting a calculation point at thecenter of the grid:

$\begin{matrix}{\frac{h_{({i,j})}^{t + 1} - h_{({i,j})}^{t}}{\Delta\; t} = {{- \frac{{{q_{x{({{i + {1/2}},j})}}^{t} \cdot \Delta}\; y_{{i + {1/2}},j}} + {{q_{x{({{i - {1/2}},j})}}^{t} \cdot \Delta}\; y_{{i - {1/2}},j}}}{\Delta\;{\overset{\_}{x} \cdot \Delta}\;\overset{\_}{y}}} - \frac{{{q_{y{({i,{j + {1/2}}})}}^{t} \cdot \Delta}\; x_{i,{j + {1/2}}}} + {{q_{y{({i,{j - {1/2}}})}}^{t} \cdot \Delta}\; x_{i,{j - {1/2}}}}}{\Delta\;{\overset{\_}{x} \cdot \Delta}\;\overset{\_}{y}} + P - I}} & (10)\end{matrix}$wherein Δt is the time step of calculation; Δx and Δy are the lengths ofthe calculation grid unit in the x and y direction, respectively; and Δxand Δy with the subscripts are the real lengths of the cell in the x andy directions, respectively.

Since every element may have more than a single inflow cell or more thana single outflow cell, applying a first order backward differenceoperator to equation (1) allows rewriting the unit inflow dischargegetting into a cell during Δt in the x direction as:q _(x(i−1/2,j)) ^(t) ·Δy _(i−1/2,j)=[max(q _(x(i−1,j),)0)Δy_(i−1/2,j)]−[min(q _(x(i+1,j)),0)Δy _(i+1/2,j)]  (11)q _(y(i,j−1/2)) ^(t) ·Δx _(i,j−1/2)=[max(q _(y(i,j−1),)0)Δx_(i,j−1/2)]−[min(q _(y(i,j+1)),0)Δx _(i,j+1/2)]  (12)

where Δx_(i,j−1/2), Δy_(i−1/2j), Δx_(i,j+1/2) and Δy_(i+1/2j) are theactual lengths of four sides of a cell, respectively. Similarly, duringΔt, the outflow discharge components in the x and y directions can beexpressed in programmed instructions as:

$\begin{matrix}{{{q_{x{({{i + {1/2}},j})}}^{t} \cdot \Delta}\; y_{{i + {1/2}},j}} = {q_{x{({i,j})}} \cdot \begin{bmatrix}{\Delta\; y_{{i + {1/2}},j}} & q_{{x{({i,j})}} > 0} \\{\Delta\; y_{{i - {1/2}},j}} & q_{{x{({i,j})}} < 0}\end{bmatrix}}} & (13) \\{{{q_{y{({i,{j + {1/2}}})}}^{t} \cdot \Delta}\; x_{i,{j + {1/2}}}} = {q_{y{({i,j})}} \cdot \begin{bmatrix}{\Delta\; x_{i,{j + {1/2}}}} & q_{{y{({i,j})}} > 0} \\{\Delta\; x_{i,{j - {1/2}}}} & q_{{y{({i,j})}} < 0}\end{bmatrix}}} & (14)\end{matrix}$where Δx_(i,j−1/2), Δy_(i−1/2j), Δx_(i,j+1/2) and Δy_(i+1/2j) representthe side lengths of an upstream cell in the x and y directions,respectively; and Δx_(i,j+1/2), Δy_(i+1/2,j) represent the side lengthsof a downstream cell in the x and y directions, respectively.

3.1.3. GPU-Based Methods for Solving Wave Equations

In an embodiment, the AIC system is programmed to execute instructionsto solve kinematic wave equations expressed in a 2D domain, such asequation (6), by utilizing hardware capabilities of a multiple-coregraphics processing unit (“GPU”). Explicit finite volume methodstypically rely on stencil computations, and the computations employed bythe explicit finite volume methods are inherently parallel. In anembodiment, the computations are performed by a many-core graphicsprocessing unit (“GPU”).

A stencil computation consists of an iterated assignment to elements ofan array by an expression that involves arrays indexed by some functionof the indices used to assign to the target.

In an embodiment, an efficient hardware-adapted shallow water simulationis executed by a GPU. The simulation is based on a high-resolutioncentral-upwind scheme implemented in programmed instructions. Theapproach presented herein may be extended to other architectures thatare similar to the GPU-based architecture and/or otherhyperbolic-conservation-laws-based architectures.

FIG. 8B depicts an example approach for solving shallow water equationsusing a multi-core graphics processing unit. In the depicted example, aGPU pipeline includes the processing that is performed by a multi-coreGPU and that results in computing water position and depth data, whichin turn is provided to the CPU.

In an embodiment, a GPU of a GPU pipeline 880, receives, in a buffer882, water position and depth data. The GPU also receives, in a buffer884, field topography data. The water position and depth data, stored inbuffer 882, and the topography data, stored in buffer 884, is uniformedby the GPU, as shown in a process arrow 886. A uniform is a globalShader variable declared with the “uniform” storage qualifier. They actas parameters that the user of a shader program can pass to the program.Their values are stored in a program object. Uniforms are so namedbecause they do not change from one shader invocation to the next withina particular rendering call. This makes them unlike shader stage inputsand outputs, which are often different for each invocation of a shaderstage.

In an embodiment, surface flow data 888, erosion data 890 and absorptiondata 892, is uniformed by the GPU, as shown in a process arrow 894.

In an embodiment, the uniformed output generated by process 886 and theuniformed output generated by process 894 are combined and used tocompute, as shown in a process arrow 896, new water position and depthdata and new topography data. The new water position and depth data isstored in buffer 898, while the new topography data is stored in abuffer 899.

Once the water position and depth buffers are computed inside the GPU,the resulting data, stored in buffers 898 and 899, are passed onto theCPU and merged with data computed as previously. More specifically, thedata computed in the previous timestep is merged with the currenttimestep computation returned from the GPU.

FIG. 8C depicts an example flowchart for solving shallow water equationsfor an agricultural field using a multi-core graphics processingapproach.

In step 854, shallow water equations for determining a subfield moisturemodel for a field are solved using a GPU-bases accelerator. To port theshallow water equations to the GPU-based accelerator, a 3D meshrepresenting the field is produced and ported to the GPU. An exampleprocess is described in steps 858-868 of FIG. 8C.

In step 858, a CV-based triangular mesh is extracted from a targetfield. The 3D mesh may be produced based on existing field data andcomputer vision (“CV”) data that can be used to perform the watershedcomputations. The CV data may be acquired, or otherwise obtained, byextracting high-dimensional data from the images depicting the field inorder to produce a numerical representation of the field. This mayinclude transformation of the images into 3D meshed geometry.

In step 860, a normalized map of elevations is generated for the field.

In step 862, a 3D model of the field is created. The 3D model mayinclude the vertex displacement normals mapped onto the vertices.

In step 864, a water density and distribution normalized map isgenerated based on, at least in part, the 3D model of the field.

In step 866 water velocities and diffusion for the watershed arecomputed based on the water density and distribution normalized map andthe 3D model.

In step 868, the changes in position and volume over the densitydistributions are determined. The changes are normalized, and thenintegrated with other changes over the density distributions.

If, in step 870, it is determined that the equations have been solvedfor the entire map, then step 820, shown in FIG. 8A, is executed.Otherwise, the steps 860-868 are repeated until the shallow equationsfor the entire map are computed.

3.1.4. Generating an Overland Flow Model for a Field

Referring again to FIG. 8A, in step 820, the AIC system generates anoverland flow model for the field based on the results obtained byexecuting the shallow water equations and the kinematic waveapproximation equations. This step corresponds to generating overlandflow model 716, described in FIG. 7 .

The time step for the model may be calculated using the expression:

$\begin{matrix}{{\delta\; t} = \frac{N_{co}\delta\; x}{\sqrt{u^{2} + v^{2}}}} & (15)\end{matrix}$where N_(co) is a user-specified value for the Courant number. Thetypically used N_(co) values are 0.2 so that the value of δt higher than0.3 seconds is not exceeded.

Steps 826-830 provide an example application of using the overland flowmodel for the field. The example application allows querying the systemfor a moisture level or a nitrogen level for a particular point in thefield and generating an answer to the query based on the data stored inthe overland flow model.

In step 826, the AIC system determines whether a query is received.Examples of queries include a query for a moisture level for aparticular point in the field, a query for a nitrogen level for aparticular point, and so forth. The query may be provided to the AICsystem via a graphical user interface that is configured to provideinteractivity with the AIC system and the data models generated by theAIC system.

If, in step 828, the AIC system determines that the query was received,then the AIC system proceeds to performing step 830; otherwise, the AICsystem proceeds to performing step 826, in which the AIC system awaitsreceiving a query.

In step 830, the AIC system generates an answer to the query andprovides the answer to the graphical user interface for displaying theanswer on a display device. Generating an answer to the query mayinclude querying the overland flow model for the moisture levelinformation or the nitrogen level information for the particular pointin the field. The answer may be expressed in a form of a data point, ina form of a 2D map for the area that includes the particular data point,in a form of a table, and the like.

3.2. Precipitation and Infiltration Data

In an embodiment, implementations of the process for generating anoverland water flow model for an entire field assume that precipitationis spatially uniform over the field. This is a reasonable assumption forthe fields that are rather small in size.

In an embodiment, a field centroid is identified, and hourly rainfalldata provided for the field centroid is used to model infiltration andrunoff rates for the field.

In an embodiment, infiltration is modeled using the Green and Amptequation. The Green and Ampt model is based on water flux and assumesthat the infiltration is proportional to the total head gradient. TheGreen-Ampt equation for infiltration capacity I_(c) can be written as:

$\begin{matrix}{I_{c} = {K_{s}( {1 + \frac{( {h + \psi_{f}} )( {\phi - \theta_{i}} )}{F}} )}} & (16)\end{matrix}$

where I_(c) is the infiltration capacity (L/T), K_(s) is the saturatedhydraulic conductivity (L/T), ψ_(f) is the capillary pressure head atthe wetting front (L), F is the total infiltrated depth (L), φ is thetotal porosity, h is the water depth (L), and θ_(i) is the initial soilmoisture content.

Further, it is assumed that the soil properties are spatially uniformwithin a pixel domain. The initial soil moisture content may be modeledusing, for example, the Darcy-Buckingham model.

3.3. Topography Processing

FIG. 9 depicts an example flowchart for determining an overland flowmodel for an agricultural field. The process may be executed by AICsystem 130 described in FIG. 1 . To perform the process depicted in FIG.9 , AIC system 130 may execute code instructions 180, such as slopes andcurvature modeling instructions 136, shallow water instructions 137,kinematic wave instructions 138, and overland flow modeling instructions139.

In step 802, an AIC system receives precipitation and infiltration datafor an agricultural field. Step 802 is described in FIG. 8A.

In step 902, the AIC system receives field geometry data for the field.The field geometry data is usually a high-granularity topographical mapof the field, such as per-subfield map, or a fine grid of 5 m by 5 mcells.

In step 904, the AIC system generates a digital elevation model from thefield geometry data. This may include rasterization of the fieldgeometry data and saving the digital mesh of data points in the digitalelevation model.

In step 906, the AIC generates a filtered digital elevation model fromthe digital elevation model. Filtering the digital elevation model togenerate a filtered digital elevation model may include removing, fromthe digital elevation model, the data points that are outliers or noise.

The input raster elevation datasets created from lidar point cloudsoften contain noise introduced during the raster creation process. Toremove the noise data points and enhance features of our interest, suchas furrows or local depressions, a nonlinear filtering of the elevationvalues may be performed.

In an embodiment, filtering of the topography data begins by reading theinput bare earth digital elevation model. The mean slope of the rawinput digital elevation model is computed to determine whether theterrain is steep (slope≥5°) or not (slope≤5°). The slope steepnessthreshold value can be modified if desired. If the terrain is steep,then a geometric curvature method is used to filter the data. Otherwise,a Laplacian curvature method is used to filter the data.

When analyzing high resolution topography data to extract variousfeatures of interest, the filtering may be performed to regularize theelevation data. This may include removing the unwanted data points toreduce the small-scale surface variability, while maintaining andenhancing the features of interest. In the case of channel networkextraction, this may allow filtering out the bumpiness of the ground,while preserving the features such as sharp channel banks.

In an embodiment, the filtering is performed using a filter that isexpressed using the following linear diffusion equation:

$\begin{matrix}{\frac{\partial{z( {x,y,t} )}}{\partial t} = {\nabla{\cdot ( {c{\nabla h}} )}}} & (17)\end{matrix}$where z (x, y, t) is the elevation data at time t, c is the diffusioncoefficient, and V is the gradient operator. The linear diffusionequation is isotropic as the diffusion coefficient c is constant inspace and time. While simple to apply, this may result in blurring theterrain edges and losing the sharpness and localization of the terrain'sfeatures.

A diffusion filter expressed in equation (17) may be configured tosmooth irregularities, such as noise data points, while retaining thefeatures of interest, by defining the diffusion coefficient c as afunction of space and time. This operation results in a nonlineardiffusion equation:

$\begin{matrix}{\frac{\partial{z( {x,y,i} )}}{\partial i} = {\nabla{\cdot \lbrack {{c( {x,y,i} )}{\nabla z}} \rbrack}}} & (18)\end{matrix}$

A dimensionless form of the nonlinear diffusion equation (18) may beused with a dimensionless diffusion coefficient c and a time parameterscaled by diffusion. This may be expressed in L² units, wherein idenotes the number of filtering iterations performed on the image inequation (18). Two possible forms of the diffusion coefficient are:

$\begin{matrix}{c = e^{({- {({{{\nabla z}}/\lambda})}^{2}})}} & (19) \\{c = \frac{1}{1 + ( \frac{{\nabla z}}{\lambda} )^{2}}} & (20)\end{matrix}$where |∇z| is the absolute value of the elevation gradient at locationx, y and time t, and lambda is the edge-stopping threshold computed asthe 90th quantile of the gradient distribution.

Equations (19) and (20) are also called edge stopping functions and maybe referred to as a PM1 function and a PM2 function, respectively. ThePM1 and PM2 promote diffusion within boundaries of the terrain featuresand allow preserving the features' edges.

Another filtering approach uses a nonlinear diffusion filter thatinvolves convolution of the landscape with a small Gaussian kernel ateach time step. This approach may use a filter in which the time step isset based on the von Neumann stability criterion:

$\begin{matrix}{{\delta\; t} \leq {\frac{1}{4}( {\delta\; x} )^{2}}} & (21)\end{matrix}$where δx is the pixel size. Since the time parameter is indiffusion-scaled L² units, equation (21) is dimensionally consistent.

In step 908, the AIC system extracts slopes and curvatures data from thefiltered digital elevation model.

Ridges, hillslopes, and valleys can be identified from high resolutiontopography data by comparing the probability density function (“pdf”) ofcurvature to the standard Gaussian distribution on a quantile-quantile(qq) plot. The qq-plot indicates whether a sample is drawn from theGaussian distribution, or how the plot deviates from the Gaussiandistribution.

To determine the curvatures data, any of two curvature definitions maybe used. A first definition is the Laplacian y defined as the gradientof the elevation gradient ∇z:γ=∇² z  (22)

A second definition is the geometric curvature κ defined as:κ=∇·(∇z/|∇z|)  (23)where the elevation gradient is normalized by its magnitude. Thegradients are estimated with a central difference operator, except atthe edges where a single-sided difference is used.

The geometric curvature is more effective in identifying convergentfeatures in natural landscapes than the Laplacian approach. On the otherhand, the Laplacian approach performs better than the geometriccurvature approach for terrains that include engineered areas with amixture of natural channels and artificial features such as ditches androads.

In step 910, the AIC system determines flow accumulation data based onthe filtered digital elevation data. The flow accumulation data mayinclude flow information such as the locations of the flows, theirdepths, their water velocity, and so forth.

In step 912, the AIC system determines watershed data and subfieldgeometry data. The watershed data may include information aboutwatersheds. This may include locations of the watersheds, andcharacteristics of the watersheds.

In step 804, the AIC system executes shallow water equations andkinematic wave equations over the subfield geometry data. Step 804 isdescribed in detail in FIG. 8A.

In step 820, the AIC system generates an overland flow model for thefield. Step 820 is described in detail in FIG. 8A.

In step 824, the AIC system updates the overland flow model based on theprecipitation and infiltration data. This step is performed if theprecipitation and infiltration data has not been used to generate theoverland flow model already. However, if the precipitation andinfiltration data has been already used to generate the overland flowmodel, then step 824 is omitted.

Steps 826-830 provide an example application of using the overland flowmodel for the field. The example application allows querying the systemfor a moisture level or a nitrogen level for a particular point in thefield and generating an answer to the query based on the data stored inthe overland flow model. Steps 826-830 are described in FIG. 8A.

4. Example Applications

FIG. 10 is a block diagram that illustrates an example computer systemconfigured to generate and display graphical user interface configuredto interact with a subfield moisture modeling tool. The depictedcomputer system includes a subfield moisture modeling tool 1000 and agraphical user interface 1010. The depicted computer system alsoincludes one or more computer-based processors (not depicted in FIG. 10), one or more memory units (not depicted in FIG. 10 ), and one or morestorage devices (not depicted in FIG. 10 ).

Subfield moisture modeling tool 1000 includes all, or at least some,components 702-726 that are described in detail in FIG. 7 . The tool maybe generated by executing software applications on the processors of thecomputer system. The tool may be stored as an executable program, aweb-based application, an application served from a server, anapplication served from a cloud storage system, and the like.

Graphical user interface 1010 is an interface generated to provideinteractivity with subfield moisture modeling tool 1000. Graphical userinterface 1010 may be used to facilitate entering a query to subfieldmoisture modeling tool 1000, and obtaining, from subfield moisturemodeling tool 1000, a response to the query. Graphical user interface1010 may be a component that is separate from subfield moisture modelingtool 1000, as depicted in FIG. 10 . Alternatively, graphical userinterface 1010 may be a component of subfield moisture modeling tool1000.

In an embodiment, graphical user interface 1010 is configured togenerate and display graphical objects that may be used to providequeries to subfield moisture modeling tool 1000. The queries may includerequests for specific information for particular areas of anagricultural field, particular points of the field, particular zones ofthe fields, and the like. Examples of the queries may include a query1030 for moisture level information for a particular point in the field,and a query 1050 for nitrogen level data for a particular point in thefield.

Graphical user interface 1010 may be configured to communicate thereceived queries to subfield moisture modeling tool 1000, and uponreceiving responses to the queries, communicate the received responsesto requestors. For example, in response to receiving moisture level datafor a particular point, graphical user interface 1010 may display themoisture level data response 1040 on a display device. According toanother example, in response to receiving nitrogen level data for aparticular point, graphical user interface 1019 may display the nitrogenlevel data response 1060 on a display device.

Graphical user interface 1010 may be configured to receive, fromsubfield moisture modeling tool 1000 various notifications and todisplay the notification on display devices. For example, graphical userinterface 1010 may be configured to receive notifications indicatingstanding water present in a particular subfield, and in response toreceiving the notifications, display the notification 1020 on a displaydevice.

5. Benefits of Certain Embodiments

In an embodiment, the overland flow model is used to improve predictionsof hydrologic fluxes in agricultural fields. Furthermore, it may be usedto provide notifications of standing water events following strongrainfalls. It may also be used to generate warnings to avoid locationssusceptible to ponding during breeding trials. Moreover, the modeloutput layer may be used as a covariate in scripting tools used togenerate agricultural prescription for the fields. Examples ofagricultural prescriptions include a seed density prescription, a hybridselection prescription, and a nitrogen fertilizer prescription. It mayalso be used to explain variabilities in crop yields between wet and dryyears, and to explain how the variability relates to subfield moistureof the soil.

Using the techniques described herein, a computer may generate and makeavailable moisture level information for the soil in an agriculturalfield with the accuracy and efficiency that otherwise is not achievable.For example, without the presented techniques, a complex model requiringa large amount of initial input would be required to capture all aspectsof a moisture level information for high-granularity topographical mapsof the field. Thus, the techniques described herein improve uponprevious moisture level modeling techniques by reducing the amount ofdata required to generate accurate moisture level information,increasing the efficiency with which the digital models of moisturelevels for the soil are run, and increasing the locational accuracy ofthe moisture level information.

6. Extensions and Alternatives

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

What is claimed is:
 1. A computer-implemented data processing method forgenerating overland flow models, the method comprising: executing aplurality of shallow water calculations using a depth-averagedintegration over subfield geometry data to determine surface water depthdata, surface water velocity data, and surface water discharge data foran agricultural field; executing hardware-implemented kinematic waveapproximation equations, over a 2D grid of the subfield geometry data,the surface water depth data, the surface water velocity data, and thesurface water discharge data to generate an overland flow model thatincludes moisture levels for the agricultural field; and based on, atleast in part, the overland flow model, generating and causingdisplaying a visual graphical image of the agricultural field comprisinga plurality of color pixels having color values corresponding to themoisture levels determined for the agricultural field.
 2. The method ofclaim 1, wherein output of the overland flow model is provided to acontrol computer of a fertilizer spreader, dispatched in theagricultural field, to adjust nitrogen application instructions executedby the fertilizer spreader for applying fertilizers to the agriculturalfield.
 3. The method of claim 2, wherein the output of the overland flowmodel is provided to an irrigation control computer of an irrigationsystem installed in the agricultural field to adjust watering settingsand amounts of water sprayed over the agricultural field.
 4. The methodof claim 2, wherein the output of the overland flow model is provided tocontrol computers of planters or seeders, dispatched in the agriculturalfield, to adjust amounts of seeds to be planted in the agriculturalfield.
 5. The method of claim 2, wherein the output of the overland flowmodel is provided to control computers of planters, seeders, harvesters,combines or cultivators, dispatched in the agricultural field, tocontrol paths within the agricultural field that the planters, seeders,harvesters or cultivators follow as they plant seeds or harvest crops;wherein the output of the overland flow model is provided to anintelligence computer system to generate agricultural prescriptions,status reports, summaries, or other field-specific documents.
 6. Themethod of claim 1, further comprising: receiving digital field geometrydata specifying a geometry of the agricultural field; based at least inpart on the digital field geometry data, determining digital elevationmodel data for the agricultural field; based at least in part on thedigital elevation model data, determining filtered digital elevationmodel data; extracting, from the filtered digital elevation model data,slope data and curvature data that represent physical topographicalfeatures of the agricultural field; and based at least in part on thefiltered digital elevation model data, determining watershed datarepresenting one or more watersheds and determining the subfieldgeometry data of the agricultural field.
 7. The method of claim 1,wherein the plurality of shallow water calculations and wavecalculations include one or more kinematic wave calculations and one ormore shallow water calculations; wherein the one or more kinematic wavecalculations are performed using one or more of: a finite volume methodor an accelerator-based multi-core graphics processing unit(“GPU”)-based method.
 8. The method of claim 1, further comprising:based on, at least in part, the overland flow model, determining thatany standing water subarea is present in the agricultural field; inresponse to determining, based on the overland flow model, that one ormore standing water subareas are present in the agricultural field:generating one or more standing water notifications indicating the oneor more standing water subareas present in the agricultural field; andgenerating and causing displaying a standing water visual graphicalimage of the one or more standing water subareas present in theagricultural field including standing water subarea color pixels havingcolor values corresponding to the one or more standing water subareas.9. The method of claim 1, further comprising: receiving a first requestfor providing a first soil moisture level value for a first subarea ofthe agricultural field; based on, at least in part, the overland flowmodel, determining the first soil moisture level value for the firstsubarea of the agricultural field; and generating and causing displayinga first subarea visual graphical image of the first subarea of theagricultural field including first subarea color pixels corresponding tothe first soil moisture level value for the first subarea.
 10. Themethod of claim 1, further comprising: receiving a second request forproviding a first nitrogen level value for a second subarea of theagricultural field; based on, at least in part, the overland flow model,determining the first nitrogen level value for the second subarea of theagricultural field; and generating and causing displaying a secondsubarea visual graphical image of the second subarea of the agriculturalfield including second subarea color pixels corresponding to the firstnitrogen level value for the second subarea.
 11. The method of claim 1,further comprising: at a plurality of different times, determining soilmoisture levels for the plurality of different times, and repeating thereceiving, the executing, and the generating and causing displaying thevisual graphical image of the agricultural field including the colorpixels corresponding to the soil moisture levels for a plurality ofdifferent times.
 12. The method of claim 1, wherein the subfieldgeometry data for the agricultural field is digitally stored as ahigh-resolution geometry map sampled at any type of resolution.
 13. Themethod of claim 1, wherein the overland flow model provides a same-daydistribution of water fluxes across a surface of the agricultural fieldand a multi-date distribution of shallow groundwater across theagricultural field.
 14. One or more non-transitory computer-readablestorage media storing one or more sequences of instructions which, whenexecuted by one or more processors, cause the one or more processors toperform: executing a plurality of shallow water calculations using adepth-averaged integration over subfield geometry data to determinesurface water depth data, surface water velocity data, and surface waterdischarge data for an agricultural field; executing hardware-implementedkinematic wave approximation equations, over a 2D grid of the subfieldgeometry data, the surface water depth data, the surface water velocitydata, and the surface water discharge data to generate an overland flowmodel that includes moisture levels for the agricultural field; andbased on, at least in part, the overland flow model, generating andcausing displaying a visual graphical image of the agricultural fieldcomprising a plurality of color pixels having color values correspondingto the moisture levels determined for the agricultural field.
 15. Thenon-transitory computer-readable storage media of claim 14, whereinoutput of the overland flow model is provided to a control computer of afertilizer spreader, dispatched in the agricultural field, to adjustnitrogen application instructions executed by the fertilizer spreaderfor applying fertilizers to the agricultural field.
 16. Thenon-transitory computer-readable storage media of claim 15, wherein theoutput of the overland flow model is provided to an irrigation controlcomputer of an irrigation system installed in the agricultural field toadjust watering settings and amounts of water sprayed over theagricultural field.
 17. The non-transitory computer-readable storagemedia of claim 14, further comprising sequences of instructions which,when executed, cause the one or more processors to perform: receivingdigital field geometry data specifying a geometry of the agriculturalfield; based at least in part on the digital field geometry data,determining digital elevation model data for the agricultural field;based at least in part on the digital elevation model data, determiningfiltered digital elevation model data; extracting, from the filtereddigital elevation model data, slope data and curvature data thatrepresent physical topographical features of the agricultural field; andbased at least in part on the filtered digital elevation model data,determining watershed data representing one or more watersheds anddetermining the subfield geometry data of the agricultural field. 18.The non-transitory computer-readable storage media of claim 14, whereinthe plurality of shallow water calculations and wave calculationsinclude one or more kinematic wave calculations and one or more shallowwater calculations; and wherein the one or more kinematic wavecalculations are performed using one or more of: a finite volume methodor an accelerator-based multi-core graphics processing unit(“GPU”)-based method.
 19. The non-transitory computer-readable storagemedia of claim 14, further comprising sequences of instructions which,when executed, cause the one or more processors to perform: based on, atleast in part, the overland flow model, determining that any standingwater subarea is present in the agricultural field; in response todetermining, based on the overland flow model, that one or more standingwater subareas are present in the agricultural field: generating one ormore standing water notifications indicating the one or more standingwater subareas present in the agricultural field; and generating andcausing displaying a standing water visual graphical image of the one ormore standing water subareas present in the agricultural field includingstanding water subarea color pixels having color values corresponding tothe one or more standing water subareas.
 20. The non-transitorycomputer-readable storage media of claim 14, further comprisingsequences of instructions which, when executed, cause the one or moreprocessors to perform: receiving a first request for providing a firstsoil moisture level value for a first subarea of the agricultural field;based on, at least in part, the overland flow model, determining thefirst soil moisture level value for the first subarea of theagricultural field; and generating and causing displaying a firstsubarea visual graphical image of the first subarea of the agriculturalfield including first subarea color pixels corresponding to the firstsoil moisture level value for the first subarea.
 21. The non-transitorycomputer-readable storage media of claim 14, further comprisingsequences of instructions which, when executed, cause the one or moreprocessors to perform: receiving a second request for providing a firstnitrogen level value for a second subarea of the agricultural field;based on, at least in part, the overland flow model, determining thefirst nitrogen level value for the second subarea of the agriculturalfield; and generating and causing displaying a second subarea visualgraphical image of the second subarea of the agricultural fieldincluding second subarea color pixels corresponding to the firstnitrogen level value for the second subarea.
 22. The non-transitorycomputer-readable storage media of claim 14, further comprisingsequences of instructions which, when executed, cause the one or moreprocessors to perform: at a plurality of different times, determiningsoil moisture levels for the plurality of different times, and repeatingthe receiving, the executing, and the generating and causing displayingthe visual graphical image of the agricultural field including the colorpixels corresponding to the soil moisture levels for a plurality ofdifferent times.
 23. The non-transitory computer-readable storage mediaof claim 14, wherein the subfield geometry data for the agriculturalfield is digitally stored as a high-resolution geometry map sampled atany type of resolution.
 24. The non-transitory computer-readable storagemedia of claim 14, wherein the overland flow model provides a same-daydistribution of water fluxes across a surface of the agricultural fieldand a multi-date distribution of shallow groundwater across theagricultural field.